Introduction to R and RStudio
Introduction
General information
Programming language that allows you to:
- manipulate data: import, transform, export, etc.
- carry out more or less complex statistical analyses: description, exploration, modelization…
- create (pretty) figures
Features:
- available on Windows, Mac and Linux.
- free and open source
- large user community/online help.
- large number of specific packages.
History:
- 1993: start of the R project
- 2000: release of R 1.0.0
- 2024: R 4.3.3
Fun fact (not so fun) about why R is better than excel:
In 2020, “Covid
: le Royaume-Uni passe à côté de milliers de cas à cause… d’un fichier
Excel arrivé à saturation”
More information comparing R and excel here.
The RGui
After installing R, you can use it by double-clicking on the R icon
As you can see, the default R interface (RGui console) is not very user friendly. So it is better to use complementary software that will function as a graphical interface between you and R. This interface is a kind of shell that makes R work in the background. Several graphical interfaces have been developed, but the most used and practical is RStudio.
Rstudio
First sight
Studio displays
4 large panes. Their position may be changed based on your preference.
Here are default:
| left | right | |
|---|---|---|
| upper | Script pane | Environment/History pane |
| lower | Console pane | Help/Plots/Files |
Info: your four panes may be blank while two of mines are filled with text. We’ll come on that later.
The console pane (lower left)
This
is a simple R console, like in the RGui (see previous section).
Warning: Here, is an example provided by the bioinformatics plateform BiGR. Your local RStudio might differ: the version of R, the list of available packages, etc. On your local machine, RStudio console will match with the RGui.
Let’s try to enter the command print():
[1] "Hello World"
We just used a function, called print.
This function tries to print on screen everything provided between
parenthesis ( and ). In this particular case,
we gave the character string "Hello World", and the
function print successfully printed it on screen !
Now click on Session -> Save Workspace as and save the current work space (I named mine “my_session.RData”). What append in the R console pane? You saw it! A command has been automatically written. For me, it is:
When you need help with R, whether on a function error, on a script result or anything alike, please save your work space and send-it to your favorite R-developer. This contains everything you did in your session.
Info: There is a syntax coloration, there is a good autocompletion and parameter suggestion. All bioinformaticians use auto-completion, but shhh it’s our secret
The environment/history pane (upper right)
This pane has three main tabs: Environment, History and Connections (the tab Tutorial was added recently).
Environment
Environment lists every single variable, object or data loaded in R. This includes only what you typed yourself and does not include environment variables. Example; in you console pane, enter the following command:
What append in the Environment pane ? You’re right: a variable is now available!
When a more complex object is declared in your work space, then some general information may be available. Example:
You can see the dataframe. Click on it to have a preview of the data
it contains, then click on the light-blue arrow have a deeper insight of
its content:
Now click on Session -> Clear Work space: and see your work disappear. This action cannot be undone. While it is useful to clear one work space from time to time in order to avoid name space collisions, it is better to save your work space before.
History
This tab is quite important: while you test and search in the console, your history keeps a track of each command line you entered. This will definitely help you to build your scripts, to pass your command lines to your coworkers, and to revert possible unfortunate errors.
Each history is related to a session. You may see many commands in your history. Some of them are not even listed in your console. R Studio in writes there every command, even the ones that were masked for the sake of your eyes (knitting commands, display commands, help commands, etc.)
Please note that your history has a limit and only saves the latest command lines.
The help/plots/files pane (lower right)
This pane has four main tabs: Files, Plots, Packages and Help (the tab Viewer is not really used, and Presentation was added recently).
Help
This is maybe the most important pane of your R Studio. THIS is the difference between R Studio and another code editor. Search for any function here and not on the internet. This pane shows you the available help for YOUR version of R, YOUR version of a given package.
Concurrent version might have both different default parameters and different interfaces. Please be sure over the internet, to copy and type commands that are not harmfull for your computer.
Never ever copy code from the internet right to your console Why? Example: https://www.wizer-training.com/blog/copy-paste
Files
Just like any file explorer, we can move accross directories, create folders and file, delete them, etc.
Or use the function dir.create():
You should change your working directory right now:
Or use setwd():
You can delete files:
or use the function file.remove():
Packages
Here is listed all installed packages, with their description and their version.
More information about packages in a next section.
Plots
If you work with R scripts, the graphs will be displayed here.
The script pane (upper left)
This is where you write your R scripts. This also accepts other languages (e.g. bash, python, …), but R Studio shines for its R integration.
Please, please ! Write your commands in the Script pane, then execute them by hitting CTRL + Enter. This is very much like your lab-workbook: the history panel only keeps a limited number of function in memory while this script keeps your commands in a file on your disk. You may share it, edit it, comment it, etc.
The extension for a script in R is .R or.r
(or .Rmd for special Rmarkdown script), for example
my_script.R.
TLDR – Too Long Didn’t Read
Graphic interface presentation :
- Write command lines in Script pane (upper left)
- Execute command lines by hitting CTRL + Enter from script pane et see them in the console.
- Have a look at the environment and history in case on the upper right pane
- Search for help in the lower right pane.
R – Basics
Variables and types
Numbers
Remember, a variable is the name given to a value stored in memory.
Example 3, the number three, exists in R. You can store it
in a variable with the arrow operator <-:
With the code above, the number 3 is stored in a variable called “three”. You can do this in R with anything. Literally anything. Whole files, pipelines, images, anything.
Maths in R works the same as your regular calculator:
[1] 6
[1] -1
[1] 2
[1] 12
[1] 3
Info:
#is the way to write a comment into your script. The instruction after#, and on the same line, will not be executed by R. For example in3 + three # Add,3 + threewill be executed, but# Addwill not. A good coder uses comments a lot to explain the calculations made.
Characters
Characters are delimited with quotes: either double " or
' simple:
four <- "4"
five <- '5'
# The example below is a very good example of
# how to never ever name a variable.
シ <- "happy"Mathematics do not work with characters at all … Try the following:
You can try to turn characters in numbers with the function: as.numeric():
[1] 5
[1] 5
A function is a R command that is followed by parenthesis
( and ). Between these parenthesis, we enter
arguments. Use the help pane to have information about the list
of arguments expected and/or understood by a given function.
As said previously, you can store any of the previously typed commands in a variable:
[1] 5
[1] 2
Please! Please! Give your variable a name understandable by humans. I don’t want to see any of you calling their variable “a”, “b”, “my_awsome_var”, …
Tricky Question:
I have two numbers: mysterious_number_7, and
suspicious_number_7. When I apply the function print()
on them, it return 7. They are both numeric. However, they
are not equal … Why ?
[1] 7
[1] 7
[1] TRUE
[1] TRUE
# Check that values of mysterious_number_7 and suspicious_number_7 are equal
mysterious_number_7 == suspicious_number_7[1] FALSE
# Check that values of mysterious_number_7 and suspicious_number_7 are identical
identical(mysterious_number_7, suspicious_number_7)[1] FALSE
We will talk about difference between equality and identity later.
Answer
This is due to the number of digits displayed in R. You are very likely to have issues with that in the future, as all (bio)informatician around the world.
[1] 7
[1] 7
[1] FALSE
[1] FALSE
You can change the number of displayed digits with the function options():
options(digits=100)
Data structures
Until now, we have seen simple information stored into a variable. But we can create a more complexe structure in order to store several information into a single variable.
Vector
You can make vectors and tables in R. Don’t panic, there will be no maths in this presentation.
In R, vectors are created with the function c():
[1] "1" "2" "3" "4" "10" "20"
[1] TRUE
One can select an element of the vector with squared brackets
[ and ]:
[1] "1"
One can select multiple elements of a vector with ::
[1] "2" "3" "4"
Question 1: Is there a difference between these two vectors ?
Answer
There is a difference indeed: c_vector contains characters, n_vector contains numeric.
[1] "1" "2" "3"
[1] 1 2 3
[1] FALSE
[1] TRUE
[1] FALSE
You can always use the function identical()
to test equality with robustness and exactitude.
You may have learned about the operator == for equality.
But this is not perfect, look at our example:
[1] TRUE TRUE TRUE
The operator == is not aware of types.
Another example, mixing numeric and boolans:
[1] TRUE
[1] FALSE
In computer science, there is a reason why boolean and integers are mixed. We won’t cover this reason now. It’s out of our scope. Feel free to ask if you’re interested in history and maths. ###TO DO!!!
Question 2: Can I include both text and numbers in a vector ?
Answer
No. We can not mix types in a vector. Either all its content is made of number or all its content is made of characters.
Here, all our values have been turned into characters:
[1] "1" "2" "3"
[1] FALSE
[1] TRUE
[1] FALSE
[1] TRUE
Above, the function all()
returns TRUE if all its content equals to
TRUE.
Question 3: How to create an histogram with a vector ?
Help
A simple way to visualize your data is to use a graph. The functionhist()
may help you (of course, use the Help pane!!).
Data Frame
In R, tables are created with the function data.frame():
c.1..3. c.2..4.
1 1 2
2 3 4
By default, R gives names for columns and rows.
You can rename columns and row names respectively with functions colnames()
and rownames().
colnames(one2three4) <- c("Col_1_3", "Col_2_4")
rownames(one2three4) <- c("Row_1_2", "Row_3_4")
print(one2three4) Col_1_3 Col_2_4
Row_1_2 1 2
Row_3_4 3 4
You can access a column and a line of the data frame using squared
brackets [ and ]. Use the following syntax:
[row, column]. Use either the name of the row/column or its
position.
Col_1_3 Col_2_4
Row_1_2 1 2
Col_1_3 Col_2_4
Row_1_2 1 2
[1] 1 3
[1] 1 3
[1] 1
[1] 1 3
If you like maths, you will remember the order
[row, column]. If you’re not familiar with that, then you will do like 99% of all software engineer: you will write[column, row], and you will get an error. Trust me. 99%. Remember, an error is never a problem in informatics.
Question 1: Can I mix characters and numbers in a data frame row ?
Answer
Yes, it is possible:
mixed_data_frame <- data.frame(
"Character_Column" = c("a", "b", "c"),
"Number_Column" = c(4, 5, 6)
)
print(mixed_data_frame) Character_Column Number_Column
1 a 4
2 b 5
3 c 6
The function str()
can be used to look at the types of each elements in an object.
'data.frame': 3 obs. of 2 variables:
$ Character_Column: chr "a" "b" "c"
$ Number_Column : num 4 5 6
'data.frame': 2 obs. of 2 variables:
$ Col_1_3: num 1 3
$ Col_2_4: num 2 4
Question 2: Can I mix characters and numbers in a data frame column ?
Answer
No:
mixed_data_frame <- data.frame(
"Mixed_letters" = c(1, "b", "c"),
"Mixed_numbers" = c(4, "5", 6)
)
print(mixed_data_frame) Mixed_letters Mixed_numbers
1 1 4
2 b 5
3 c 6
'data.frame': 3 obs. of 2 variables:
$ Mixed_letters: chr "1" "b" "c"
$ Mixed_numbers: chr "4" "5" "6"
Question 3: How can you add 2 for each cell of the dataframe ?
Read a table as data frame
Exercise: Use the Help pane to find how to use the function
read.csv().
You can find example_table.csv here(download it by clocking on the “Download raw file” button).
Use the function read.csv()
to:
- open the file
example_table.csv. - this table has a header (
TRUE). - this table has row names in the column called “Gene_id”.
Let all other parameters to their default values.
Save the opened table in a variable called
example_table.
Now let us explore this dataset.
We can click on environment pane:
And if you click on it:
Be careful, large table may hang your session.
Alternatively, we can use the function head()
which prints the first lines of a table:
Sample1 Sample2 Sample3 Sample4
Caml 9.998194 10.004116 9.172489 9.139667
Scamp5 9.995917 10.818685 11.417558 14.907892
Dgki 9.993974 13.664396 16.132275 17.420057
Mas1 9.993956 11.370854 11.233629 9.912863
Apba1 9.992540 14.253438 14.001228 13.654701
Phkg2 9.980898 8.748654 8.714821 9.146529
The function summary()
describes the dataset per sample:
Sample1 Sample2 Sample3 Sample4
Min. : 9.944 Min. : 6.838 Min. : 5.551 Min. : 5.844
1st Qu.: 9.953 1st Qu.: 9.000 1st Qu.: 10.120 1st Qu.: 9.779
Median : 9.971 Median : 10.954 Median : 11.326 Median : 11.905
Mean :18.937 Mean : 19.836 Mean : 20.828 Mean : 21.412
3rd Qu.: 9.994 3rd Qu.: 12.647 3rd Qu.: 12.650 3rd Qu.: 13.968
Max. :99.784 Max. :105.077 Max. :112.188 Max. :111.820
Have a look at the summary()
of the dataset per gene, using the function t()
to transpose:
Caml Scamp5 Dgki Mas1 Apba1 Phkg2 Timm8b
Sample1 9.998194 9.995917 9.993974 9.993956 9.99254 9.980898 99.78373
Sample2 10.004116 10.818685 13.664396 11.370854 14.25344 8.748654 105.07739
Sample3 9.172489 11.417558 16.132275 11.233629 14.00123 8.714821 112.18819
Sample4 9.139667 14.907892 17.420057 9.912863 13.65470 9.146529 109.09544
Capn7 Yrdc Coq10a Gm27000 Lrrc41 Acadsb Pdzd11
Sample1 9.976005 9.971093 9.970835 9.965511 9.960667 9.959179 9.952750
Sample2 11.314599 8.905508 8.820582 7.414795 9.961954 11.261520 9.031553
Sample3 11.452421 7.367243 10.449131 7.709008 10.435298 12.336088 10.700876
Sample4 11.692871 9.375526 10.865062 13.126211 9.137375 12.703318 10.832218
Smarca2 Gm26079 Ptpn5 Rexo2 Ifi27 Snhg20
Sample1 9.952224 99.51466 9.947524 9.94634 9.943989 9.943724
Sample2 9.272424 103.08963 11.090058 13.36391 12.407626 6.838499
Sample3 11.194709 109.85654 11.572261 11.47744 13.591186 5.551247
Sample4 12.117571 111.82050 10.255021 12.29288 14.906542 5.843670
Caml Scamp5 Dgki Mas1
Min. : 9.140 Min. : 9.996 Min. : 9.994 Min. : 9.913
1st Qu.: 9.164 1st Qu.:10.613 1st Qu.:12.747 1st Qu.: 9.974
Median : 9.585 Median :11.118 Median :14.898 Median :10.614
Mean : 9.579 Mean :11.785 Mean :14.303 Mean :10.628
3rd Qu.:10.000 3rd Qu.:12.290 3rd Qu.:16.454 3rd Qu.:11.268
Max. :10.004 Max. :14.908 Max. :17.420 Max. :11.371
Apba1 Phkg2 Timm8b Capn7
Min. : 9.993 Min. :8.715 Min. : 99.78 Min. : 9.976
1st Qu.:12.739 1st Qu.:8.740 1st Qu.:103.75 1st Qu.:10.980
Median :13.828 Median :8.948 Median :107.09 Median :11.384
Mean :12.975 Mean :9.148 Mean :106.54 Mean :11.109
3rd Qu.:14.064 3rd Qu.:9.355 3rd Qu.:109.87 3rd Qu.:11.513
Max. :14.253 Max. :9.981 Max. :112.19 Max. :11.693
Yrdc Coq10a Gm27000 Lrrc41
Min. :7.367 Min. : 8.821 Min. : 7.415 Min. : 9.137
1st Qu.:8.521 1st Qu.: 9.683 1st Qu.: 7.635 1st Qu.: 9.755
Median :9.141 Median :10.210 Median : 8.837 Median : 9.961
Mean :8.905 Mean :10.026 Mean : 9.554 Mean : 9.874
3rd Qu.:9.524 3rd Qu.:10.553 3rd Qu.:10.756 3rd Qu.:10.080
Max. :9.971 Max. :10.865 Max. :13.126 Max. :10.435
Acadsb Pdzd11 Smarca2 Gm26079
Min. : 9.959 Min. : 9.032 Min. : 9.272 Min. : 99.51
1st Qu.:10.936 1st Qu.: 9.722 1st Qu.: 9.782 1st Qu.:102.20
Median :11.799 Median :10.327 Median :10.573 Median :106.47
Mean :11.565 Mean :10.129 Mean :10.634 Mean :106.07
3rd Qu.:12.428 3rd Qu.:10.734 3rd Qu.:11.425 3rd Qu.:110.35
Max. :12.703 Max. :10.832 Max. :12.118 Max. :111.82
Ptpn5 Rexo2 Ifi27 Snhg20
Min. : 9.948 Min. : 9.946 Min. : 9.944 Min. :5.551
1st Qu.:10.178 1st Qu.:11.095 1st Qu.:11.792 1st Qu.:5.771
Median :10.673 Median :11.885 Median :12.999 Median :6.341
Mean :10.716 Mean :11.770 Mean :12.712 Mean :7.044
3rd Qu.:11.211 3rd Qu.:12.561 3rd Qu.:13.920 3rd Qu.:7.615
Max. :11.572 Max. :13.364 Max. :14.907 Max. :9.944
To go further
[1] 4
[1] 20
[1] 20 4
'data.frame': 20 obs. of 4 variables:
$ Sample1: num 10 10 9.99 9.99 9.99 ...
$ Sample2: num 10 10.8 13.7 11.4 14.3 ...
$ Sample3: num 9.17 11.42 16.13 11.23 14 ...
$ Sample4: num 9.14 14.91 17.42 9.91 13.65 ...
TLDR – Too Long Didn’t Read
# Declare a variable, and store a value in it:
three <- 3
# Basic operators: + - / * work as intended:
six <- 3 + 3
# Quotes are used to delimiter text:
seven <- "7"
# You cannot perform maths on text:
"7" + 8 # raises an error
seven + 8 # also raises an error
six + 8 # works fine
# You can change the type of your variable with:
as.numeric("4") # the character '4' becomes the number 4
as.character(10) # the number 10 becomes the character 10
# You can compare values with:
six < seven
six + 1 >= seven
identical(example_table, mixed_data_frame)
# You can load and save a dataframe with:
read.table(file = ..., sep = ..., header = TRUE)
write.table(x = ..., file = ...)
# Create a table with:
my_table <- data.frame(...)
# Create a vector with:
my_vector <- c(...)
# You can see the firs lines of a dataframe with:
head(example_table)
# Search for help in the help pane or with:
help(...)R – Packages
What are modules and packages
Modules and package are considered to be the same thing in this lesson. The difference is technical and does not relates to our session.
Most of the work you are likely to do with R will require one or several packages. A package is a list of functions or pipelines shipped under a given name. In general, a package groups together functions linked to an analysis theme or the same objective. Every single function you use through R comes from a package or another.
Read the very first line of the help pane:
It reads: help {utils}. The function help
comes from the package utils.
# Call the function "help", with the argument "example_table", and show only the first line
head(example_table, 1) Sample1 Sample2 Sample3 Sample4
Caml 9.998194 10.00412 9.172489 9.139667
Warning: Sometime, two package may have a function with the same name. They are most certainly not doing the same thing. IMHO, it is a good habbit to always call a function while disambiguating the package name.
utils::help()is better thanhelp()alone.
# Call the function "help" ***from the package utils***, with the argument "example_table", and show only the first line
utils::head(example_table, 1) Sample1 Sample2 Sample3 Sample4
Caml 9.998194 10.00412 9.172489 9.139667
Install a package
You may install a new package.
Use install.packages()
to install a package.
This will raise a prompt asking for simple questions : where to download from (choose somewhere in France), whether to update other packages or not, etc.
Do not be afraid by the large amount of things prompted in the console and let R do the trick.
Alternatively, you can click Tool -> Install Packages in RStudio; or click on the “install” button in the tab Packages of the pane File/Help.
You can list installed packages with installed.packages(),
and find for packages that can be updates with old.packages().
These packages can be updated with update.packages().
While the function install.packages()
searches packages in the common R package list, many bioinformatics
packages are available on other shared packages warehouses. Just like
AppleStore and GoogleStore do not have the same applications on mobile,
R has multiple sources for its packages. You need to know one of them,
and one only Bioconductor.
You can use Bioconductor with the function BiocManager::install():
Use a package
All installed packages are not activated in your working session. You
can load a package with the function library():
If the package is not installed, you will get an error.
If there is no error message, then you can try:
And search for help about how to run your command.
Alternatively, there is a more complete help page, with the function
browseVignettes().
It opens your browser automatically, and if you click on “HTML”, you get
some information about the package like functions, tutorials, etc.
Tips for your project
Write a good script
Good practice:
- make a documentation (a header at the start of the script which explains the purpose of the script, and the analysis steps for example),
- comments (uninterpreted line, begin by
#), - code indentation (spaces before code line),
- understandable variable names,
- do not nest too many functions inside each other,
### difficult to understand
print(rowMeans(data.frame(c(9, 14, 17, 9, 13),
c(11, 10, 20, 7, 17),c(15, 8, 19, 10, 15) )) )
### easy to understand
## Goal: this script computes the mean of the expression of our 3 samples for each gene:
#create a dataframe with the genes expression of our 3 samples:
example_data_frame <- data.frame("Expression_Sample_1" = c(9, 14, 17, 9, 13),
"Expression_Sample_2" = c(11, 10, 20, 7, 17),
"Expression_Sample_3" = c(15, 8, 19, 10, 15)
)
#add corresponding genes names into row names:
rownames(example_data_frame) <- c("Caml", "Scamp5", "Dgki", "Mas1", "Apba1")
#compute the mean of the expression for each gene:
mean_expression_Samples123 <- rowMeans(example_data_frame)
#print the result:
print(mean_expression_Samples123)- save your script regularly, as well as your working environment,
- save the versions of the loaded packages at the end of your analysis
(you can print loaded packages thanks to the function
sessionInfo()and save the result into a file thanks to the functioncapture.output()).
R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C
[3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8
[5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8
[7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/Paris
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] digest_0.6.35 R6_2.5.1 bookdown_0.39
[4] fastmap_1.1.1 xfun_0.43 cachem_1.0.8
[7] knitr_1.46 htmltools_0.5.8.1 rmarkdown_2.26
[10] lifecycle_1.0.4 cli_3.6.2 rmdformatsbigr_1.0.0
[13] sass_0.4.9 jquerylib_0.1.4 compiler_4.3.3
[16] highr_0.10 rstudioapi_0.15.0 tools_4.3.3
[19] evaluate_0.23 bslib_0.7.0 yaml_2.3.8
[22] jsonlite_1.8.8 rlang_1.1.3
Load and save R objects
While working on your projects, you will process datasets in R. The results of these analyses will be stored on variables. This means, that when you close RStudio, some of this work might be lost.
We already saw the function save.image()
to save a complete copy of your working environment.
However, you can save only the content of a given variable. This is useful when you want to save the result of a function (or a pipeline) but not the whole 5 hours of work you’ve been spending on how-to-make-that-pipeline-work-correctly.
The format is called: RDS
for R Data Serialization. This is done with the function saveRDS():
You can also load a RDS into a variable. This is useful when you
receive a RDS from a coworker, or you’d like to keep going your work
from a saved point. This is done with the function readRDS():
Human data
Warning: If you hold human-related genomic datasets. You cannot use/upload these data anywhere. This is illegal, and doing surch thing may lead to 5 years in prison and up to 300 000€ fine. Art. 226-16, Section 5, Code pénal.
Packages updates
It is a good practice to maintain package versions within a work project. If you update a package (whether by need, or by will), then you should restart your work from the begining. This stands as long as you’re not 100% sure the update does not affect your results.
Swirl R package
How to continue to learn R?
What is swirl?
swirl is an R
package that teaches you R programming and data science interactively,
at your own pace, and right in the R console.
It presents a choice of course lessons and interactively tutors a student through them. A student may be asked to watch a video, to answer a multiple-choice or fill-in-the-blanks question, or to enter a command in the R console precisely as if he or she were using R in practice. Emphasis is on the last, interacting with the R console. User responses are tested for correctness and hints are given if appropriate.
Progress is automatically saved so that a user may quit at any time and later resume without losing work.
Conclusion
No programming language is better than any other. Anyone saying the opposite is (over)-specialized in the language they are advertising.
In the field of bioinformatics, languages used by the community are quite limited. THere are bash, R and Python. While learning bash cannot be escaped nowadays, it is not enough to perform a complete analysis with publication ready figures and results. You should be interested in another programming language: R and/or Python. R allows you to do a lot of different analyses, and it has a large user community with lots of online help, so it’s one of the easiest languages for beginners.
Please, note that this advice is valid today, but may change. Other programming languages are used, some have lost their place on the podium, and others are trying to supersede bash, R, and Python.